On the determination of material parameters for internal variable thermoelastic–viscoplastic constitutive models |
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Authors: | A. Andrade-Campos S. Thuillier P. Pilvin F. Teixeira-Dias |
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Affiliation: | 1. Departamento de Engenharia Mecânica, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;2. Laboratoire de Génie Mécanique et Matériaux (LG2M), Université de Bretagne-Sud, Rue de Saint Maudé, BP 92116-56321 Lorient Cedex, France |
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Abstract: | The work presented in this paper deals with the determination of material parameters used in internal variable constitutive models. In order to determine the best suited material parameter set, in the less computationally expensive way, two optimization approaches are used: (i) a gradient-based method and (ii) a continuous evolutionary algorithm (EA) method. The first approach uses a combination of the steepest descent gradient and the Levenberg–Marquardt techniques. The performance of this method is known to be highly dependent on the starting set of parameters and its results are often inconsistent. The EA-based technique provides a better way to determine an optimized set of parameters (the overall minimum). Thus, the difficulty of choosing a starting set of parameters for this process is minor. The main application in this work is a 16 parameter thermoelastic–viscoplastic constitutive model. Experimental data was obtained from tensile and shear tests at different temperatures and used to compare with numerical results and to determine the correct set of material parameters. Numerical constraints were introduced to enforce physical requirements on the material parameters. Both methods are used to determine the 12 material parameters needed for an AA1050-O aluminium alloy. Although the EA-based method achieved a slightly better result, it proved to be computationally more expensive than the gradient-based method. |
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Keywords: | Parameter identification Thermoelastic&ndash viscoplastic constitutive model Optimization Gradient-based method Evolutionary algorithm |
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